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A Numerical Comparison of Finite Difference and Finite Element Methods for a Stochastic Differential Equation with Polynomial Chaos

Published online by Cambridge University Press:  28 May 2015

Ning Li
Affiliation:
College of Mathematics and Systems Science, Xinjiang University, Urumqi 830046, P.R. China
Bo Meng
Affiliation:
College of Mathematics and Systems Science, Xinjiang University, Urumqi 830046, P.R. China
Xinlong Feng
Affiliation:
College of Mathematics and Systems Science, Xinjiang University, Urumqi 830046, P.R. China
Dongwei Gui*
Affiliation:
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830001; Cele National Station of Observation & Research for Desert-Grassland Ecosystem in Xinjiang, Cele 848300, Xinjiang, P.R. China.
*
*Corresponding author. Email addresses: [email protected] (N. Li), [email protected] (B. Meng), [email protected] (X. Feng), [email protected] (D. Gui)
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Abstract

A numerical comparison of finite difference (FD) and finite element (FE) methods for a stochastic ordinary differential equation is made. The stochastic ordinary differential equation is turned into a set of ordinary differential equations by applying polynomial chaos, and the FD and FE methods are then implemented. The resulting numerical solutions are all non-negative. When orthogonal polynomials are used for either continuous or discrete processes, numerical experiments also show that the FE method is more accurate and efficient than the FD method.

Type
Research Article
Copyright
Copyright © Global-Science Press 2015 

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References

[1]Cameron, R. and Martin, W., The orthogonal development of nonlinear functionals in series of Fourier-Hermite functionals, Ann. Math. 48, 385392 (1947).Google Scholar
[2]Chihara, T., An Introduction to Orthogonal Polynomials, Gordon and Breach Publishers, New York (1978).Google Scholar
[3]Ghanem, R. and Spanos, P., Stochastic Finite Elements: A Spectral Approach, Springer Verlag, New York (1991).Google Scholar
[4]Ghanem, R., Ingredients for a general purpose stochastic finite element formulation, Comput. Methods Appl. Mech. Engrg. 168, 1934 (1999).Google Scholar
[5]Ghanem, R., Stochastic finite elements for heterogeneous media with multiple random non-Gaussian properties, ASCE J. Eng. Mech. 125, 2640 (1999).Google Scholar
[6]He, Y. and Feng, X., H1-Stability and Convergence of the FE, FV and FD methods for an elliptic equation, East Asian J. Appl. Math. 3, 154170 (2013).Google Scholar
[7]Feng, X. and He, Y., H1-Super-convergence of center finite difference method based on P1-element for the elliptic equation, Appl. Math. Model. 38, 54395455 (2014).CrossRefGoogle Scholar
[8]Ogura, H., Orthogonal functionals of the Poisson process, IEEE Trans. Inform. Theory 18, 473481 (1972).Google Scholar
[9]Schoutens, W., Stochastic Processes and Orthogonal Polynomials, Springer Verlag, New York (2000).Google Scholar
[10]Szegö, G., Orthogonal Polynomials, American Mathematical Society, Providence (1939).Google Scholar
[11]Wiener, N., The homogeneous chaos, Amer. J. Math. 60, 897936 (1938).CrossRefGoogle Scholar
[12]Xiu, D. and Karniadakis, G.E., The Wiener-Askey polynomial chaos for stochastic differential equations, SIAM J. Sci. Comput. 24, 619644 (2002).Google Scholar
[13]Xiu, D. and Karniadakis, G.E., Modeling uncertainty in steady state diffusion problems via generalised polynomial chaos, Comput. Methods Appl. Math. Eng. 191, 49274948 (2002).Google Scholar
[14]Xiu, D. and Karniadakis, G.E., A new stochastic approach to transient heat conduction modeling with uncertainty, Inter. J. Heat Mass Trans. 46, 46814693 (2003).CrossRefGoogle Scholar
[15]Xiu, D. and Karniadakis, G.E., Modeling uncertainty in flow simulations via generalised polynomial chaos, J. Comput. Phys. 187, 137167 (2003).Google Scholar
[16]Tang, T., The Hermite spectral method for Gaussian-type functions, SIAM J. Sci. Comput. 14, 594606 (1993).Google Scholar
[17]Tang, T. and Zhou, T., On discrete least square projection in unbounded domain with random evaluations and its application to parametric uncertainty quantification, SIAM J. Sci. Comput. 36, A2272A2295 (2014).Google Scholar